ClarusC64/clinical-healing-trajectory-tokenization-phase-segmentation-v0.1
What this dataset tests Whether a model can segment high-frequency recovery datainto interpretable healing phases. Required outputs phase_sequence phase_boundaries phase_confidence_0_100 Token labels acute_drop early_rebound consolidation_plateau oscillatory_instability secondary_drop delayed_rebound steady_ascent maladaptive_plateau recovery_lock_in Boundary format Use day indicesexampleacute_drop d0-d2 Typical failures naming phases without boundaries… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-healing-trajectory-tokenization-phase-segmentation-v0.1.
What this dataset tests
Whether a model can segment high-frequency recovery data into interpretable healing phases.
Required outputs
- phase_sequence
- phase_boundaries
- phaseconfidence0_100
Token labels
- acute_drop
- early_rebound
- consolidation_plateau
- oscillatory_instability
- secondary_drop
- delayed_rebound
- steady_ascent
- maladaptive_plateau
- recoverylockin
Boundary format
Use day indices example acute_drop d0-d2
Typical failures
- naming phases without boundaries
- using vague labels like "improving"
- missing secondary drops and rebounds
Suggested prompt wrapper
System
You tokenize healing trajectories into phases.
User
Insult type {insult_type}
High-frequency summary {highfrequencysummary}
Return
- phase sequence using tokens separated by ->
- phase boundaries as token dX-dY
- confidence score 0-100
- one sentence evidence
Citation
ClarusC64 dataset family